🤖 AI Summary
This work addresses the vulnerability of deep neural networks to adversarial attacks in robotic semantic segmentation, which poses significant risks to safety-critical systems. To mitigate this issue, the authors propose a dedicated adversarial attack detection method tailored to robotic perception scenarios. By integrating semantic segmentation architectures with an adversarial example detection mechanism, the approach overcomes the limitation of existing robustness research that predominantly focuses on image classification. Experimental results demonstrate that the proposed method effectively identifies and defends against adversarial attacks targeting semantic segmentation models, thereby substantially enhancing the safety and robustness of robotic systems operating in real-world environments.
📝 Abstract
Deep Neural Networks (DNNs) achieve strong performance in semantic segmentation for robotic perception but remain vulnerable to adversarial attacks, threatening safety-critical applications. While robustness has been studied for image classification, semantic segmentation in robotic contexts requires specialized architectures and detection strategies.